Re-Analysis and Meta-Analysis of Summary Statistics from Gene-Environment Interaction Studies

Authors

Duy T Pham
Kenneth E Westerman
Cong Pan
Ling Chen
Shylaja Srinivasan
Elvira Isganaitis
Mary Ellen Vajravelu
Fida Bacha
Steve Chernausek
Rose Gubitosi-Klug
Jasmin Divers
Catherine Pihoker
Santica M Marcovina
Alisa K Manning
Han Chen

Publication Date

12-1-2023

Journal

Bioinformatics

DOI

10.1093/bioinformatics/btad730

PMID

38039147

PMCID

PMC10724851

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes

Keywords

Gene-Environment Interaction, Genome-Wide Association Study, Models, Statistical, Sample Size, Data Interpretation, Statistical, Polymorphism, Single Nucleotide, Phenotype

Abstract

MOTIVATION: statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics.

RESULTS: We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene-environment interaction studies.

AVAILABILITY AND IMPLEMENTATION:REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.